| P2093 | author name string | Marina Ávila-Villanueva | |
| | Jaime Gómez-Ramírez | |
| | Miguel Ángel Fernández-Blázquez | |
| P2860 | cites work | Stress and memory: behavioral effects and neurobiological mechanisms | Q21342972 |
| | Alzheimer's disease as homeostatic responses to age-related myelin breakdown | Q22251261 |
| | Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests | Q22673963 |
| | Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease | Q24595802 |
| | Apolipoprotein E: from cardiovascular disease to neurodegenerative disorders | Q26747482 |
| | Potential of functional MRI as a biomarker in early Alzheimer's disease | Q26864734 |
| | Mild cognitive impairment: clinical characterization and outcome | Q28140626 |
| | What is cognitive reserve? Theory and research application of the reserve concept | Q28213550 |
| | Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium | Q28252389 |
| | Apolipoprotein E: cholesterol transport protein with expanding role in cell biology | Q28294791 |
| | Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) | Q29011672 |
| | The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease | Q29547554 |
| | The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease | Q29614406 |
| | Clinical and biomarker changes in dominantly inherited Alzheimer's disease | Q29619940 |
| | Bias in random forest variable importance measures: illustrations, sources and a solution | Q33270401 |
| | Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review | Q33853042 |
| | Memory concerns, memory performance and risk of dementia in patients with mild cognitive impairment | Q33895227 |
| | Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors | Q33981434 |
| | Mild cognitive impairment: disparity of incidence and prevalence estimates | Q34248749 |
| | Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine | Q34316883 |
| | Daily stressors and memory failures in a naturalistic setting: findings from the VA Normative Aging Study. | Q34537125 |
| | Binding of human apolipoprotein E to synthetic amyloid beta peptide: isoform-specific effects and implications for late-onset Alzheimer disease | Q34662682 |
| | A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease | Q35049917 |
| | Effects of sleep deprivation and aging on long-term and remote memory in mice | Q35210773 |
| | Exploring strategies to operationalize cognitive reserve: A systematic review of reviews | Q35573263 |
| | Is MCI really just early dementia? A systematic review of conversion studies. | Q35866400 |
| | The Vallecas Project: A Cohort to Identify Early Markers and Mechanisms of Alzheimer's Disease. | Q36107153 |
| | Prediction and classification of Alzheimer disease based on quantification of MRI deformation | Q36298571 |
| | The 12 Years Preceding Mild Cognitive Impairment Due to Alzheimer's Disease: The Temporal Emergence of Cognitive Decline. | Q37053812 |
| | Rate of conversion from prodromal Alzheimer's disease to Alzheimer's dementia: a systematic review of the literature | Q37255097 |
| | Outcome over seven years of healthy adults with and without subjective cognitive impairment | Q37414902 |
| | Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) conversion | Q37451840 |
| | From mild cognitive impairment to subjective cognitive decline: conceptual and methodological evolution. | Q37653048 |
| | The Risk of Incident Mild Cognitive Impairment and Progression to Dementia Considering Mild Cognitive Impairment Subtypes | Q37695131 |
| | Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: meta-analysis | Q38248954 |
| | Subjective Cognitive Decline in Preclinical Alzheimer's Disease | Q38686409 |
| | Association between educational attainment and amyloid deposition across the spectrum from normal cognition to dementia: neuroimaging evidence for protection and compensation | Q39310372 |
| | Resolving controversies on the path to Alzheimer's therapeutics | Q39709721 |
| | Mild cognitive impairment in the elderly: predictors of dementia | Q43742864 |
| | Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM. | Q45948126 |
| | Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. | Q45956072 |
| | Subjective Cognitive Decline as a Preclinical Marker for Alzheimer's Disease: The Challenge of Stability Over Time. | Q47122616 |
| | Glucose metabolism, gray matter structure, and memory decline in subjective memory impairment | Q47232869 |
| | SMOTE: Synthetic Minority Over-sampling Technique | Q47247524 |
| | A Clinically-Translatable Machine Learning Algorithm for the Prediction of Alzheimer's Disease Conversion in Individuals with Mild and Premild Cognitive Impairment | Q47553444 |
| | The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70-90 years | Q48124401 |
| | Predicting Stability of Mild Cognitive Impairment (MCI): Findings of a Community Based Sample | Q48394698 |
| | Predictive validity and diagnostic stability of mild cognitive impairment subtypes. | Q50931246 |
| | Markers for the risk of progression from mild cognitive impairment to Alzheimer's disease. | Q51663316 |
| | Predictive value of APOE-ε4 allele for progression from MCI to AD-type dementia: a meta-analysis. | Q51803759 |
| | Internal Consistency Over Time of Subjective Cognitive Decline: Drawing Preclinical Alzheimer's Disease Trajectories | Q57122680 |
| | Greedy function approximation: A gradient boosting machine | Q57532752 |
| | Machine Learning for Predicting Cognitive Diseases: Methods, Data Sources and Risk Factors | Q58111439 |
| | European Prevention of Alzheimer’s Dementia Longitudinal Cohort Study (EPAD LCS): study protocol | Q60930812 |
| | High-performance medicine: the convergence of human and artificial intelligence | Q61444255 |
| | Risk of dementia after fluctuating mild cognitive impairment: When the yo-yoing stops | Q61950932 |
| | Association of Lifestyle and Genetic Risk With Incidence of Dementia | Q78878961 |
| | Impact of individual and neighborhood dimensions of socioeconomic status on the prevalence of mild cognitive impairment over seven-year follow-up | Q89750892 |
| | Explainable machine-learning predictions for the prevention of hypoxaemia during surgery | Q90002799 |
| | Neural substrates of cognitive reserve in Alzheimer's disease spectrum and normal aging | Q90026367 |
| | The Italian INTERCEPTOR Project: From the Early Identification of Patients Eligible for Prescription of Antidementia Drugs to a Nationwide Organizational Model for Early Alzheimer's Disease Diagnosis | Q90593560 |
| | Utilizing semantic intrusions to identify amyloid positivity in mild cognitive impairment | Q90747054 |
| | State of the science on mild cognitive impairment (MCI) | Q91079405 |
| | Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts | Q92020140 |
| | Targeting soluble tumor necrosis factor as a potential intervention to lower risk for late-onset Alzheimer's disease associated with obesity, metabolic syndrome, and type 2 diabetes | Q92355038 |
| | A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures | Q92404020 |
| | Longitudinal analysis of risk factors for dementia based on Mild Cognitive Impairment Screen results and questionnaire responses from healthy Japanese individuals registered in an online database | Q92653183 |
| | Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review | Q92918611 |
| | Cardiovascular health, genetic risk, and risk of dementia in the Framingham Heart Study | Q97635947 |
| | A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis | Q104478935 |
| P433 | issue | 1 | |
| P921 | main subject | random forest | Q245748 |
| | mild cognitive impairment | Q1472703 |
| | cognitive dysfunction | Q57859955 |
| P304 | page(s) | 20630 | |
| P577 | publication date | 2020-11-26 | |
| P1433 | published in | Scientific Reports | Q2261792 |
| P1476 | title | Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods | |
| P478 | volume | 10 | |